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mobilenet-yolo-v4-syg

Use Case and High-Level Description

This is a Keras* version of mobilenet-yolov4 model designed to perform real-time vehicle detection. The weights are pretrained by BDD100k and retrained by our own dataset. For details, see the repository, paper of MobileNetV2 and YOLOv4.

Specification

Metric	Value
Type	Detection
GFLOPs	65.984
MParams	61.922
Source framework	Keras*

Accuracy

Accuracy metrics obtained on SYGDate0829’SYGDate0829.z01’’SYGDate0829.z02’’SYGDate0829.z03’’SYGDate0829.zip’ which is our own made* validation dataset for converted model.

Metric	Value
mAP	86.35%

Input

Original model

Image, name - input_1, shape - 1, 416, 416, 3, format is B, H, W, C where:

• B - batch size

• H - height

• W - width

• C - channel

Channel order is RGB. Scale value - 255.

Converted model

Image, name - input_1, shape - 1, 416, 416, 3, format is B, H, W, C where:

• B - batch size

• H - height

• W - width

• C - channel

Channel order is BGR.

Output

Original model

1. The array of detection summary info, name - separable_conv2d_22/BiasAdd, shape - 1,52,52,27. The anchor values are 12,16,  19,36,  40,28.

2. The array of detection summary info, name - separable_conv2d_30/BiasAdd, shape - 1,26,26,27. The anchor values are 36,75,  76,55,  72,146.

3. The array of detection summary info, name - separable_conv2d_38/BiasAdd, shape - 1,13,13,27. The anchor values are 142,110,  192,243,  459,401.

For each case format is B,Cx,Cy,N*14,, where - B - batch size - Cx, Cy - cell index - N - number of detection boxes for cell

Detection box has format [x,y,h,w,box_score,class_no_1, …, class_no_4], where:

• (x,y) - raw coordinates of box center, apply sigmoid function to get relative to the cell coordinates

• h,w - raw height and width of box, apply exponential function and multiply by corresponding anchors to get absolute height and width values

• box_score - confidence of detection box, apply sigmoid function to get confidence in [0,1] range

• class_no_1,…,class_no_4 - probability distribution over the classes in logits format, apply sigmoid function and multiply by obtained confidence value to get confidence of each class

Converted model

1. The array of detection summary info, name - separable_conv2d_22/separable_conv2d/YoloRegion, shape - 1, 52, 52, 27. The anchor values are 12,16,  19,36,  40,28.

2. The array of detection summary info, name - separable_conv2d_30/separable_conv2d/YoloRegion, shape - 1, 26, 26, 27. The anchor values are 36,75,  76,55,  72,146.

3. The array of detection summary info, name - separable_conv2d_38/separable_conv2d/YoloRegion, shape - 1, 13, 13, 27. The anchor values are 142,110,  192,243,  459,401.

Detection box has format [x,y,h,w,box_score,class_no_1, …, class_no_4], where:

• (x,y) - coordinates of box center relative to the cell

• h,w - raw height and width of box, apply exponential function and multiply by corresponding anchors to get absolute height and width values

• box_score - confidence of detection box in [0,1] range

• class_no_1,…,class_no_4 - probability distribution over the classes in the [0,1] range, multiply by confidence value to get confidence of each class

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• Object Detection C++ Demo

• Object Detection Python* Demo

Legal Information

The original model is distributed under the following license1:

MIT License
Copyright (c) 2021 BJTU-SYG

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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